Lightweight YOLOv5 with ShuffleNetV2 for Rice Disease Detection in Edge Computing

Abstract

This study proposes a lightweight rice disease detection model optimized for edge computing environments. The goal is to enhance the You Only Look Once (YOLO) v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency. To this end, a total of 3234 high-resolution images (2400 × 1080) were collected from three major rice diseases Rice Blast, Bacterial Blight, and Brown Spot—frequently found in actual rice cultivation fields. These images served as the training dataset. The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone, thereby resulting in both model compression and improved inference speed. Additionally, YOLOv5-P, based on PP-PicoDet, was configured as a comparative model to quantitatively evaluate performance. Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance, with an mAP 0.5 of 89.6%, mAP 0.5–0.95 of 66.7%, precision of 91.3%, and recall of 85.6%, while maintaining a lightweight model size of 6.45 MB. In contrast, YOLOv5-P exhibited a smaller model size of 4.03 MB, but showed lower performance with an mAP 0.5 of 70.3%, mAP 0.5–0.95 of 35.2%, precision of 62.3%, and recall of 74.1%. This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.